Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	120,154
2	South Dakota	110,390
3	Iowa	87,683
4	Wisconsin	87,615
5	Nebraska	84,365
6	Utah	83,154
7	Tennessee	80,945
8	Rhode Island	80,805
9	Idaho	76,913
10	Wyoming	75,513
11	Kansas	75,204
12	Montana	74,926
13	Illinois	74,527
14	Indiana	74,159
15	Minnesota	72,734
16	Arkansas	71,750
17	Oklahoma	71,414
18	Nevada	70,991
19	Alabama	70,953
20	Mississippi	69,918
21	Arizona	69,327
22	Missouri	66,345
23	New Mexico	66,127
24	Louisiana	64,648
25	Alaska	62,434
26	Florida	59,604
27	Texas	59,042
28	Kentucky	58,421
29	Georgia	57,896
30	South Carolina	57,888
31	Ohio	57,749
32	Colorado	57,000
33	Delaware	56,983
34	California	55,905
35	New Jersey	52,454
36	Michigan	51,997
37	Massachusetts	51,785
38	Connecticut	50,823
39	North Carolina	49,769
40	Pennsylvania	48,385
41	New York	48,253
42	West Virginia	45,440
43	Maryland	44,567
44	District of Columbia	40,158
45	Virginia	39,385
46	Puerto Rico	33,589
47	Washington	31,982
48	New Hampshire	30,646
49	Oregon	26,152
50	Maine	16,603
51	Hawaii	14,908
52	Vermont	11,410

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	California	1,217
2	Rhode Island	1,112
3	Oklahoma	845
4	South Carolina	843
5	Arizona	807
6	Connecticut	790
7	North Carolina	762
8	Massachusetts	725
9	Kansas	688
10	New Hampshire	678
11	Delaware	628
12	Utah	623
13	Ohio	610
14	Wyoming	598
15	Tennessee	562
16	Texas	562
17	Kentucky	536
18	Florida	506
19	New York	506
20	Nevada	496
21	West Virginia	483
22	Mississippi	438
23	Pennsylvania	437
24	District of Columbia	427
25	South Dakota	418
26	Indiana	402
27	Idaho	385
28	Alabama	371
29	Michigan	371
30	Alaska	363
31	Arkansas	360
32	Georgia	360
33	Missouri	351
34	New Jersey	340
35	Minnesota	338
36	Maryland	337
37	Nebraska	332
38	New Mexico	323
39	Virginia	319
40	Montana	316
41	Illinois	315
42	Louisiana	289
43	Colorado	284
44	Wisconsin	274
45	Washington	256
46	Iowa	251
47	Puerto Rico	233
48	Oregon	195
49	Maine	191
50	Vermont	181
51	North Dakota	146
52	Hawaii	58

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,099
2	New York	1,908
3	Massachusetts	1,763
4	North Dakota	1,675
5	Connecticut	1,655
6	Rhode Island	1,644
7	South Dakota	1,634
8	Louisiana	1,578
9	Mississippi	1,557
10	Illinois	1,379
11	Michigan	1,276
12	Iowa	1,186
13	Pennsylvania	1,180
14	Arkansas	1,172
15	Indiana	1,171
16	Arizona	1,163
17	New Mexico	1,136
18	District of Columbia	1,092
19	South Carolina	1,004
20	Florida	992
21	Georgia	976
22	Nevada	965
23	Alabama	961
24	Tennessee	955
25	Maryland	949
26	Texas	939
27	Minnesota	925
28	Missouri	923
29	Delaware	922
30	Kansas	874
31	Wisconsin	869
32	Montana	867
33	Nebraska	834
34	Colorado	815
35	Idaho	772
36	Ohio	733
37	West Virginia	704
38	Wyoming	699
39	Kentucky	642
40	North Carolina	629
41	California	620
42	Oklahoma	602
43	Virginia	569
44	New Hampshire	525
45	Puerto Rico	455
46	Washington	433
47	Utah	381
48	Oregon	340
49	Alaska	262
50	Maine	242
51	Vermont	206
52	Hawaii	199

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Wyoming	18
2	Michigan	11
3	New Mexico	11
4	Rhode Island	11
5	Arkansas	10
6	Connecticut	10
7	Massachusetts	9
8	Illinois	8
9	Mississippi	8
10	South Carolina	8
11	District of Columbia	7
12	Tennessee	7
13	Kentucky	6
14	Minnesota	6
15	New Hampshire	6
16	New York	6
17	South Dakota	6
18	Idaho	5
19	Indiana	5
20	Nebraska	5
21	North Carolina	5
22	Pennsylvania	5
23	California	4
24	Florida	4
25	Kansas	4
26	Louisiana	4
27	Maryland	4
28	Oklahoma	4
29	Vermont	4
30	Colorado	3
31	Nevada	3
32	Ohio	3
33	Alabama	2
34	Arizona	2
35	Delaware	2
36	Montana	2
37	New Jersey	2
38	North Dakota	2
39	Puerto Rico	2
40	Texas	2
41	West Virginia	2
42	Wisconsin	2
43	Georgia	1
44	Maine	1
45	Missouri	1
46	Utah	1
47	Virginia	1
48	Washington	1
49	Alaska	0
50	Hawaii	0
51	Iowa	0
52	Oregon	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	269,757	1	99
Norton	Kansas	218,429	2	99
Lincoln	Arkansas	214,988	3	99
Dewey	South Dakota	213,340	4	99
Bon Homme	South Dakota	213,158	5	99
Davidson	Tennessee	96,124	325	89
Richland	South Carolina	61,803	1563	50
York	South Carolina	52,289	2048	34
Orange	California	49,730	2167	31
Pierce	Washington	29,585	2828	9

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	7,587	1	99
Jerauld	South Dakota	7,452	2	99
Dickey	North Dakota	6,568	3	99
Gregory	South Dakota	6,213	4	99
Foster	North Dakota	5,919	5	99
Richland	South Carolina	799	1785	43
Davidson	Tennessee	791	1801	42
York	South Carolina	634	2081	33
Orange	California	581	2180	30
Pierce	Washington	348	2607	17

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.